{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple Time Series, Pre-trained Models and Covariates\n",
    "\n",
    "This notebook serves as a tutorial for:\n",
    "\n",
    "* Training a single model on multiple time series\n",
    "* Using a pre-trained model to obtain forecasts for any time series unseen during training\n",
    "* Training and using a model using covariates\n",
    "\n",
    "First, some necessary imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# fix python path if working locally\n",
    "from utils import fix_pythonpath_if_working_locally\n",
    "\n",
    "fix_pythonpath_if_working_locally()\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from darts import TimeSeries\n",
    "from darts.utils.timeseries_generation import (\n",
    "    gaussian_timeseries,\n",
    "    linear_timeseries,\n",
    "    sine_timeseries,\n",
    ")\n",
    "from darts.models import (\n",
    "    RNNModel,\n",
    "    TCNModel,\n",
    "    TransformerModel,\n",
    "    NBEATSModel,\n",
    "    BlockRNNModel,\n",
    ")\n",
    "from darts.metrics import mape, smape\n",
    "from darts.dataprocessing.transformers import Scaler\n",
    "from darts.utils.timeseries_generation import datetime_attribute_timeseries\n",
    "from darts.datasets import AirPassengersDataset, MonthlyMilkDataset\n",
    "\n",
    "# for reproducibility\n",
    "torch.manual_seed(1)\n",
    "np.random.seed(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read Data\n",
    "Let's start by reading two time series - one containing the monthly number of air passengers, and another containing the monthly milk production per cow. These time series have not much to do with each other, except that they both have a monthly frequency with a marked yearly periodicity and upward trend, and (completely coincidentaly) they contain values of a comparable order of magnitude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "series_air = AirPassengersDataset().load()\n",
    "series_milk = MonthlyMilkDataset().load()\n",
    "\n",
    "series_air.plot(label=\"Number of air passengers\")\n",
    "series_milk.plot(label=\"Pounds of milk produced per cow\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing\n",
    "Usually neural networks tend to work better on normalised/standardised data. Here we'll use the `Scaler` class to normalise both of our time series between 0 and 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scaler_air, scaler_milk = Scaler(), Scaler()\n",
    "series_air_scaled = scaler_air.fit_transform(series_air)\n",
    "series_milk_scaled = scaler_milk.fit_transform(series_milk)\n",
    "\n",
    "series_air_scaled.plot(label=\"air\")\n",
    "series_milk_scaled.plot(label=\"milk\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train / Validation split\n",
    "Let's keep the last 36 months of both series as validation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_air, val_air = series_air_scaled[:-36], series_air_scaled[-36:]\n",
    "train_milk, val_milk = series_milk_scaled[:-36], series_milk_scaled[-36:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Global Forecasting Models\n",
    "Darts contains many forecasting models, but not all of them can be trained on several time series. The models that support training on multiple series are called *global* models. At the time of writing, there are 5 global models:\n",
    "\n",
    "* BlockRNNModel\n",
    "* RNNModel\n",
    "* Temporal Convolutional Networks (TCNs)\n",
    "* N-Beats\n",
    "* Transformer model\n",
    "\n",
    "In the following, we will distinguish two sorts of time series:\n",
    "\n",
    "* The **target time series** is the time series we are interested to forecast (given its history)\n",
    "* A **covariate time series** is a time series which may help in the forecasting of the target series, but that we are not interested in forecasting. It's sometimes also called *external data*.\n",
    "\n",
    "We further differentiate covariates series, depending on whether they can be known in advance or not:\n",
    "\n",
    "* **Past Covariates** denote time series whose past values are known at prediction time. These are usually things that have to be measured or observed.\n",
    "* **Future Covariates** denote time series whose future values are already known at prediction time for the span of the forecast horizon. These can for instance represent known future holidays, or weather forecasts. \n",
    "\n",
    "Some models use only past covariates, others use only future covariates, and some models might use both. We will dive deeper in this topic in some other notebook, but for now it is enough to know this:\n",
    "\n",
    "* `BlockRNNModel`, `TCNModel`, `NBEATSModel` and `TransformerModel` all use `past_covariates`.\n",
    "* `RNNModel` uses `future_covariates`.\n",
    "\n",
    "All of the global models listed above support training on multiple series. In addition, they also all support *multivariate series*. This means that they can seamlessly be used with time series of more than one dimension; the target series can contain one (as is often the case) or several dimensions. A time series with several dimensions is  really just a regular time series where the values at each time stamps are vectors instead of scalars.\n",
    "\n",
    "As an example, the 4 models supporting `past_covariates` follow a \"block\" architecture. They contain a neural network that takes chunks of time series in input, and outputs chunks of (predicted) future time series values. The input dimensionality is the number of dimensions (components) of the target series, plus the number of components of all the covariates - stacked together. The output dimensionality is simply the number of dimensions of the target series:\n",
    "![](static/images/global_io_covs.png)\n",
    "\n",
    "The `RNNModel` works differently, in a recurrent fashion (which is also why they support future covariates).\n",
    "The good news is that as a user, we don't have to worry too much about the different model types and input/output dimensionalities. The dimensionalities are automatically inferred for us by the model based on the training data, and the support for past or future covariates is simply handled by the `past_covariates` or `future_covariates` arguments. \n",
    "\n",
    "We'll still have to specify two important parameters when building our models:\n",
    "\n",
    "* `input_chunk_length`: this is the length of the lookback window of the model; so each output will be computed by the model by reading the previous `input_chunk_length` points.\n",
    "* `output_chunk_length`: this is the length of the outputs (forecasts) produced by the internal model. However, the `predict()` method of the \"outer\" Darts model (e.g., the one of `NBEATSModel`, `TCNModel`, etc) can be called for a longer time horizon. In these cases, if `predict()` is called for a horizon longer than `output_chunk_length`, the internal model will simply be called repeatedly, feeding on its own previous outputs in an auto-regressive fashion. If `past_covariates` are used it requires these covariates to be known for long enough in advance.\n",
    "\n",
    "### Example with One Series\n",
    "Let's look at a first example. We'll build an N-BEATS model that has a lookback window of 24 points (`input_chunk_length=24`) and predicts the next 12 points (`output_chunk_length=12`). We chose these values so it'll make our model produce successive predictions for one year at a time, looking at the past two years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_air = NBEATSModel(\n",
    "    input_chunk_length=24, output_chunk_length=12, n_epochs=200, random_state=0\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model can be used like any other Darts forecasting model, beeing fit on a single time series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-08-01 12:58:54,271] INFO | darts.models.torch_forecasting_model | Train dataset contains 73 samples.\n",
      "[2021-08-01 12:58:54,271] INFO | darts.models.torch_forecasting_model | Train dataset contains 73 samples.\n",
      "[2021-08-01 12:58:54,329] INFO | darts.models.torch_forecasting_model | Time series values are 64-bits; casting model to float64. If training is too slow you can try casting your data to 32-bits.\n",
      "[2021-08-01 12:58:54,329] INFO | darts.models.torch_forecasting_model | Time series values are 64-bits; casting model to float64. If training is too slow you can try casting your data to 32-bits.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f21dc37badc8430ebc9f9bee6a3a08f7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss: 0.0002\r"
     ]
    }
   ],
   "source": [
    "model_air.fit(train_air, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And like any other Darts forecasting models, we can then get a forecast by calling `predict()`. Note that below, we are calling `predict()` with a horizon of 36, which is longer than the model internal `output_chunk_length` of 12. That's not a problem here - as explained above, in such a case the internal model will simply be called auto-regressively on its own outputs. In this case, it will be called three times so that the three 12-points outputs make up the final 36-points forecast - but all of this is done transparently behind the scenes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE = 8.91%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred = model_air.predict(n=36)\n",
    "\n",
    "series_air_scaled.plot(label=\"actual\")\n",
    "pred.plot(label=\"forecast\")\n",
    "plt.legend()\n",
    "print(\"MAPE = {:.2f}%\".format(mape(series_air_scaled, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Process (behind the scenes)\n",
    "So what happened when we called `model_air.fit()` above?\n",
    "\n",
    "In order to train the internal neural network, Darts first makes a dataset of inputs/outputs examples from the provided time series (in this case: `series_air_scaled`). There are several ways this can be done and Darts contains a few different dataset implementations in the `darts.utils.data` package. \n",
    "\n",
    "By default, `NBEATSModel` will instantiate a `darts.utils.data.PastCovariatesSequentialDataset`, which simply builds all the consecutive pairs of input/output sub-sequences (of lengths `input_chunk_length` and `output_chunk_length`) existing in the series).\n",
    "\n",
    "For an example series of length 14, with `input_chunk_length=4` and `output_chunk_length=2`, it looks as follows:\n",
    "![](static/images/seq_dataset_one_ts.png)\n",
    "\n",
    "For such a dataset, a series of length `N` would result in a \"training set\" of `N - input_chunk_length - output_chunk_length + 1` samples. In the toy example above, we have `N=14`, `input_chunk_length=4` and `output_chunk_length=2`, so the number of samples used for training would be K = 9. In this context, a training *epoch* consists in complete pass (possibly consisting of several mini-batches) over all the samples.\n",
    "\n",
    "Note that different models are susceptible to use different datasets by default. For instance, `darts.utils.data.HorizonBasedDataset` is inspired by the [N-BEATS paper](https://arxiv.org/abs/1905.10437) and produces samples that are \"close\" to the end of the series, possibly even ignoring the beginning of the series.\n",
    "\n",
    "If you have the need to control the way training samples are produced from `TimeSeries` instances, you can implement your own training dataset by inheriting the abstract `darts.utils.data.TrainingDataset` class. Darts datasets are inheriting from torch `Dataset`, which means it's easy to implement lazy versions that do not load all data in memory at once. Once you have your own instance of a dataset, you can directly call the `fit_from_dataset()` method, which is supported by all global forecasting models.\n",
    "\n",
    "## Training a Model on Multiple Time Series\n",
    "All this machinery can be seamlessly used with multiple time series. Here's how a sequential dataset with `input_chunk_length=4` and `output_chunk_length=2` looks for two series of lengths N and M:\n",
    "\n",
    "![](static/images/seq_dataset_multi_ts.png)\n",
    "\n",
    "Note a few things here:\n",
    "\n",
    "* The different series do not need to have the same length, or even to share the same time stamps.\n",
    "* In fact, they don't even need to have the same frequency.\n",
    "* The total number of samples in the training dataset will be the union of all the training samples contained in each series; so a training epoch will now span all samples from all series.\n",
    "\n",
    "\n",
    "### Training on Both Air Traffic and Milk Series\n",
    "Let's look at another example where we fit another model instance on our two time series (air passengers and milk production). Since using two series of (roughly) the same length (roughly) doubles the training dataset size, we will use half of the number of epochs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_air_milk = NBEATSModel(\n",
    "    input_chunk_length=24, output_chunk_length=12, n_epochs=100, random_state=0\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, fitting the model on two (or more) series is as simple as giving a list of series (instead of a single series) in argument to the `fit()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-08-01 13:00:37,731] INFO | darts.models.torch_forecasting_model | Train dataset contains 194 samples.\n",
      "[2021-08-01 13:00:37,731] INFO | darts.models.torch_forecasting_model | Train dataset contains 194 samples.\n",
      "[2021-08-01 13:00:37,835] INFO | darts.models.torch_forecasting_model | Time series values are 64-bits; casting model to float64. If training is too slow you can try casting your data to 32-bits.\n",
      "[2021-08-01 13:00:37,835] INFO | darts.models.torch_forecasting_model | Time series values are 64-bits; casting model to float64. If training is too slow you can try casting your data to 32-bits.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d2a2da1a663947589780a490bc734d9f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss: 0.0008\r"
     ]
    }
   ],
   "source": [
    "model_air_milk.fit([train_air, train_milk], verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Producing Forecasts After the End of a Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, importantly, when computing the forecasts we have to specify which time series we want to forecast the future for.\n",
    "\n",
    "We didn't have this constraint earlier. When fitting models on one series only, the model remembers this series internally, and if `predict()` is called without the `series` argument, it returns a forecast for the (unique) training series. This does not work anymore as soon as a model is fit on more than one series - in this case the `series` argument of `predict()` becomes mandatory.\n",
    "\n",
    "So, let's say we want to predict future of air traffic. In this case we specify `series=train_air` to the `predict()` function in order to say we want to get a forecast for what comes after `train_air`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE = 5.83%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred = model_air_milk.predict(n=36, series=train_air)\n",
    "\n",
    "series_air_scaled.plot(label=\"actual\")\n",
    "pred.plot(label=\"forecast\")\n",
    "plt.legend()\n",
    "print(\"MAPE = {:.2f}%\".format(mape(series_air_scaled, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wait... does this mean that milk consumption helps to predict air traffic??\n",
    "Well, in this particular instance with this model, it seems to be the case (at least in terms of MAPE error). This is not so weird if you think about it, though. Air traffic is heavily characterized by the yearly seasonality and upward trend. The milk series exhibits these two traits as well, and in this case it's probably helping the model to capture them.\n",
    "\n",
    "Note that this points towards the possibility of *pre-training* forecasting models; training models once and for all and later using them to forecast series that are not in the train set.\n",
    "With our toy model we can really forecast the future values of any other series, even series never seen during training. For the sake of example, let's say we want to forecast the future of some arbitrary sine wave series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "any_series = sine_timeseries(length=50, freq=\"M\")\n",
    "pred = model_air_milk.predict(n=36, series=any_series)\n",
    "\n",
    "any_series.plot(label='\"any series, really\"')\n",
    "pred.plot(label=\"forecast\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This forecast isn't good (the sine doesn't even have a yearly seasonality), but you get the idea.\n",
    "\n",
    "Similar to what is supported by the `fit()` function, we can also give a list of series in argument to the `predict()` function, in which case it will return a list of forecast series. For example, we can get the forecasts for both the air traffic and the milk series in one go as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_list = model_air_milk.predict(n=36, series=[train_air, train_milk])\n",
    "for series, label in zip(pred_list, [\"air passengers\", \"milk production\"]):\n",
    "    series.plot(label=f\"forecast {label}\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two series returned correspond to the forecasts after then end of `train_air` and `train_milk`, respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Covariates Series\n",
    "\n",
    "Until now, we have only been playing with models that only use the history of the *target* series to predict its future. However, as explained above, the global Darts models also support the use of *covariates* time series. These are time series of \"external data\", which we are not necessarily interested in predicting, but which we would still like to feed as input of our models because they can contain valuable information.\n",
    "\n",
    "#### Building Covariates\n",
    "Let's see a simple example with our air and milk series, where we'll try to use the year and month-of-the-year as covariates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# build year and month series:\n",
    "air_year = datetime_attribute_timeseries(series_air_scaled, attribute=\"year\")\n",
    "air_month = datetime_attribute_timeseries(series_air_scaled, attribute=\"month\")\n",
    "\n",
    "milk_year = datetime_attribute_timeseries(series_milk_scaled, attribute=\"year\")\n",
    "milk_month = datetime_attribute_timeseries(series_milk_scaled, attribute=\"month\")\n",
    "\n",
    "# stack year and month to obtain series of 2 dimensions (year and month):\n",
    "air_covariates = air_year.stack(air_month)\n",
    "milk_covariates = milk_year.stack(milk_month)\n",
    "\n",
    "# scale them between 0 and 1:\n",
    "scaler_dt_air = Scaler()\n",
    "air_covariates = scaler_dt_air.fit_transform(air_covariates)\n",
    "\n",
    "scaler_dt_milk = Scaler()\n",
    "milk_covariates = scaler_dt_milk.fit_transform(milk_covariates)\n",
    "\n",
    "# split in train/validation sets:\n",
    "air_train_covariates, air_val_covariates = air_covariates[:-36], air_covariates[-36:]\n",
    "milk_train_covariates, milk_val_covariates = (\n",
    "    milk_covariates[:-36],\n",
    "    milk_covariates[-36:],\n",
    ")\n",
    "\n",
    "# plot the covariates:\n",
    "plt.figure()\n",
    "air_covariates.plot()\n",
    "plt.title(\"Air traffic covariates (year and month)\")\n",
    "\n",
    "plt.figure()\n",
    "milk_covariates.plot()\n",
    "plt.title(\"Milk production covariates (year and month)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Good, so for each target series (air and milk), we have built a covariates series having the same time axis and containing the year and the month.\n",
    "\n",
    "Note that here the covariates series are **multivariate time series**: they contain two dimensions - one dimension for the year and one for the month.\n",
    "\n",
    "### Training with Covariates\n",
    "Let's revisit our example again, this time with covariates. We will build a `BlockRNNModel` here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_cov = BlockRNNModel(\n",
    "    model=\"LSTM\",\n",
    "    input_chunk_length=24,\n",
    "    output_chunk_length=12,\n",
    "    n_epochs=300,\n",
    "    random_state=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, to train the model with covariates, it is as simple as providing the covariates (in form of a list matching the target series) as `future_covariates` argument to the `fit()` function. The argument is named `future_covariates` to remind us that the model can use future values of these covariates in order to make a prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-08-01 13:02:19,888] INFO | darts.models.torch_forecasting_model | Train dataset contains 194 samples.\n",
      "[2021-08-01 13:02:19,888] INFO | darts.models.torch_forecasting_model | Train dataset contains 194 samples.\n",
      "[2021-08-01 13:02:19,893] INFO | darts.models.torch_forecasting_model | Time series values are 64-bits; casting model to float64. If training is too slow you can try casting your data to 32-bits.\n",
      "[2021-08-01 13:02:19,893] INFO | darts.models.torch_forecasting_model | Time series values are 64-bits; casting model to float64. If training is too slow you can try casting your data to 32-bits.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a2574da8260d4202856be2bf2b4c6fe5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/300 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss: 0.0025\r"
     ]
    }
   ],
   "source": [
    "model_cov.fit(\n",
    "    series=[train_air, train_milk],\n",
    "    past_covariates=[air_train_covariates, milk_train_covariates],\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forecasting with Covariates\n",
    "similarly, getting a forecast is now only a matter of specifying the `future_covariates` argument to the `predict()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_cov = model_cov.predict(n=36, series=train_air, past_covariates=air_covariates)\n",
    "\n",
    "series_air_scaled.plot(label=\"actual\")\n",
    "pred_cov.plot(label=\"forecast\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that here we called `predict()` with a forecast horizon `n` that is larger than the `output_chunk_length` we trained our model with. We were able to do this because even though `BlockRNNModel` uses past covariates, in this case these covariates are also known into the future, so Darts is able to compute the forecasts auto-regressively for `n` time steps in the future.\n",
    "\n",
    "### Backtesting with Covariates\n",
    "We can also backtest the model using covariates. Say for instance we are interested in evaluating the running accuracy with a horizon of 12 months, starting at 75% of the air series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f6adea5dd5db46fca378559da214ab51",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/48 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE (using covariates) = 8.41%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "backtest_cov = model_cov.historical_forecasts(\n",
    "    series_air_scaled,\n",
    "    past_covariates=air_covariates,\n",
    "    start=0.6,\n",
    "    forecast_horizon=12,\n",
    "    stride=1,\n",
    "    retrain=False,\n",
    "    verbose=True,\n",
    ")\n",
    "\n",
    "series_air_scaled.plot(label=\"actual\")\n",
    "backtest_cov.plot(label=\"forecast\")\n",
    "plt.legend()\n",
    "print(\"MAPE (using covariates) = {:.2f}%\".format(mape(series_air_scaled, backtest_cov)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A few more words on past covariates, future covariates and other conditioning\n",
    "At the moment Darts supports covariates that are themselves time series. These covariates are used as model inputs, but are never themselves subject to prediction. The covariates do not necessarily have to be aligned with the target series (e.g. they do not need to start at the same time). Darts will use the actual time values of the `TimeSeries` time axes in order to jointly slice the targets and covariates correctly, both for training and inference. Of course the covariates still need to have a sufficient span, otherwise Darts will complain. \n",
    "\n",
    "As explained above, `TCNModel`, `NBEATSModel`, `BlockRNNModel`, `TransformerModel` use past covariates (they will complain if you try using `future_covariates`). If these past covariates happen to also be known into the future, then these models are also able to produce forecasts for `n > output_chunk_length` (as shown above for `BlockRNNModel`) in an auto-regressive way.\n",
    "\n",
    "By contrast, `RNNModel` uses future covariates (it will complain if you try specifying `past_covariates`). This means that prediction with this model requires the covariates (at least) `n` time steps into the future after prediction time.\n",
    "\n",
    "Past and future covariates (as well as the way they are consummed by the different models) an important but non-trivial topic, and we plan to dedicate a future notebook (or article) to explain this further.\n",
    "\n",
    "At the time of writing, Darts does not support covariates that are not time series - such as for instance class label informations or other conditioning variables. One trivial (although likely suboptimal) way to go around this is to build time series filled with constant values encoding the class labels. Supporting more general types of conditioning is a future feature on the Darts development roadmap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
